116
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Responses of Gut Microbiota and Glucose and Lipid Metabolism to Prebiotics in Genetic Obese and Diet-Induced Leptin-Resistant Mice

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          OBJECTIVE

          To investigate deep and comprehensive analysis of gut microbial communities and biological parameters after prebiotic administration in obese and diabetic mice.

          RESEARCH DESIGN AND METHODS

          Genetic ( ob/ob) or diet-induced obese and diabetic mice were chronically fed with prebiotic-enriched diet or with a control diet. Extensive gut microbiota analyses, including quantitative PCR, pyrosequencing of the 16S rRNA, and phylogenetic microarrays, were performed in ob/ob mice. The impact of gut microbiota modulation on leptin sensitivity was investigated in diet-induced leptin-resistant mice. Metabolic parameters, gene expression, glucose homeostasis, and enteroendocrine-related L-cell function were documented in both models.

          RESULTS

          In ob/ob mice, prebiotic feeding decreased Firmicutes and increased Bacteroidetes phyla, but also changed 102 distinct taxa, 16 of which displayed a >10-fold change in abundance. In addition, prebiotics improved glucose tolerance, increased L-cell number and associated parameters (intestinal proglucagon mRNA expression and plasma glucagon-like peptide-1 levels), and reduced fat-mass development, oxidative stress, and low-grade inflammation. In high fat–fed mice, prebiotic treatment improved leptin sensitivity as well as metabolic parameters.

          CONCLUSIONS

          We conclude that specific gut microbiota modulation improves glucose homeostasis, leptin sensitivity, and target enteroendocrine cell activity in obese and diabetic mice. By profiling the gut microbiota, we identified a catalog of putative bacterial targets that may affect host metabolism in obesity and diabetes.

          Related collections

          Most cited references 19

          • Record: found
          • Abstract: found
          • Article: not found

          Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex.

          We constructed error-correcting DNA barcodes that allow one run of a massively parallel pyrosequencer to process up to 1,544 samples simultaneously. Using these barcodes we processed bacterial 16S rRNA gene sequences representing microbial communities in 286 environmental samples, corrected 92% of sample assignment errors, and thus characterized nearly as many 16S rRNA genes as have been sequenced to date by Sanger sequencing.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models.

            Increased efficiency of energy harvest, due to alterations in the gut microbiota (increased Firmicutes and decreased Bacteroidetes), has been implicated in obesity in mice and humans. However, a causal relationship is unproven and contributory variables include diet, genetics and age. Therefore, we explored the effect of a high-fat (HF) diet and genetically determined obesity (ob/ob) for changes in microbiota and energy harvesting capacity over time. Seven-week-old male ob/ob mice were fed a low-fat diet and wild-type mice were fed either a low-fat diet or a HF-diet for 8 weeks (n=8/group). They were assessed at 7, 11 and 15 weeks of age for: fat and lean body mass (by NMR); faecal and caecal short-chain fatty acids (SCFA, by gas chromatography); faecal energy content (by bomb calorimetry) and microbial composition (by metagenomic pyrosequencing). A progressive increase in Firmicutes was confirmed in both HF-fed and ob/ob mice reaching statistical significance in the former, but this phylum was unchanged over time in the lean controls. Reductions in Bacteroidetes were also found in ob/ob mice. However, changes in the microbiota were dissociated from markers of energy harvest. Thus, although the faecal energy in the ob/ob mice was significantly decreased at 7 weeks, and caecal SCFA increased, these did not persist and faecal acetate diminished over time in both ob/ob and HF-fed mice, but not in lean controls. Furthermore, the proportion of the major phyla did not correlate with energy harvest markers. The relationship between the microbial composition and energy harvesting capacity is more complex than previously considered. While compositional changes in the faecal microbiota were confirmed, this was primarily a feature of high-fat feeding rather than genetically induced obesity. In addition, changes in the proportions of the major phyla were unrelated to markers of energy harvest which changed over time. The possibility of microbial adaptation to diet and time should be considered in future studies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Comparative Analysis of Pyrosequencing and a Phylogenetic Microarray for Exploring Microbial Community Structures in the Human Distal Intestine

              Background Variations in the composition of the human intestinal microbiota are linked to diverse health conditions. High-throughput molecular technologies have recently elucidated microbial community structure at much higher resolution than was previously possible. Here we compare two such methods, pyrosequencing and a phylogenetic array, and evaluate classifications based on two variable 16S rRNA gene regions. Methods and Findings Over 1.75 million amplicon sequences were generated from the V4 and V6 regions of 16S rRNA genes in bacterial DNA extracted from four fecal samples of elderly individuals. The phylotype richness, for individual samples, was 1,400–1,800 for V4 reads and 12,500 for V6 reads, and 5,200 unique phylotypes when combining V4 reads from all samples. The RDP-classifier was more efficient for the V4 than for the far less conserved and shorter V6 region, but differences in community structure also affected efficiency. Even when analyzing only 20% of the reads, the majority of the microbial diversity was captured in two samples tested. DNA from the four samples was hybridized against the Human Intestinal Tract (HIT) Chip, a phylogenetic microarray for community profiling. Comparison of clustering of genus counts from pyrosequencing and HITChip data revealed highly similar profiles. Furthermore, correlations of sequence abundance and hybridization signal intensities were very high for lower-order ranks, but lower at family-level, which was probably due to ambiguous taxonomic groupings. Conclusions The RDP-classifier consistently assigned most V4 sequences from human intestinal samples down to genus-level with good accuracy and speed. This is the deepest sequencing of single gastrointestinal samples reported to date, but microbial richness levels have still not leveled out. A majority of these diversities can also be captured with five times lower sampling-depth. HITChip hybridizations and resulting community profiles correlate well with pyrosequencing-based compositions, especially for lower-order ranks, indicating high robustness of both approaches. However, incompatible grouping schemes make exact comparison difficult.
                Bookmark

                Author and article information

                Journal
                Diabetes
                diabetes
                diabetes
                Diabetes
                Diabetes
                American Diabetes Association
                0012-1797
                1939-327X
                November 2011
                17 October 2011
                : 60
                : 11
                : 2775-2786
                Affiliations
                1Metabolism and Nutrition Research Group, Louvain Drug Research Institute, Université Catholique de Louvain, Brussels, Belgium
                2Genomic Research Laboratory, Geneva University Hospitals, Geneva, Switzerland
                3Laboratory of Microbiology, Wageningen University, Wageningen, the Netherlands
                4TI Food and Nutrition, Wageningen University, Wageningen, the Netherlands
                5Bioanalysis and Pharmacology of Bioactive Lipids Laboratory, Louvain Drug Research Institute, Université Catholique de Louvain, Brussels, Belgium
                6Laboratory of Microbial Ecology and Technology, Ghent University, Ghent, Belgium
                7Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland
                8Laboratory of Bacteriology, Geneva University Hospitals, Geneva, Switzerland
                Author notes
                Corresponding author: Patrice D. Cani, patrice.cani@ 123456uclouvain.be .

                A.E. and V.L. contributed equally to this work.

                Article
                0227
                10.2337/db11-0227
                3198091
                21933985
                © 2011 by the American Diabetes Association.

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

                Product
                Categories
                Metabolism

                Endocrinology & Diabetes

                Comments

                Comment on this article